Evidence map›Paper›PMID 38932375›Full record

ArticleVaccines2024

Building Data Triangulation Capacity for Routine Immunization and Vaccine Preventable Disease Surveillance Programs to Identify Immunization Coverage Inequities.

Audrey Rachlin, Oluwasegun Joel Adegoke, Rajendra Bohara, Edson Rwagasore, Hassan Sibomana, Adeline Kabeja, Ines Itanga, Samuel Rwunganira, Blaise Mafende Mario, Nahimana Marie Rosette and 7 more

Abstract read
In one paragraph

Article in Vaccines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Audrey RachlinGlobal Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.ORCID 0000-0002-0738-4241
Oluwasegun Joel AdegokeGlobal Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Rajendra BoharaWorld Health Organization, Dhaka 1212, Bangladesh.
Edson RwagasoreRwanda Biomedical Centre, Ministry of Health, Kigali P.O. Box 7162, Rwanda.
Hassan SibomanaRwanda Biomedical Centre, Ministry of Health, Kigali P.O. Box 7162, Rwanda.
Adeline KabejaRwanda Biomedical Centre, Ministry of Health, Kigali P.O. Box 7162, Rwanda.
Ines ItangaRwanda Biomedical Centre, Ministry of Health, Kigali P.O. Box 7162, Rwanda.
Samuel RwunganiraAfrican Field Epidemiology Network, Kigali, Rwanda.
Blaise Mafende MarioHealth Information Systems Program (HISP), Kigali, Rwanda.ORCID 0009-0009-7346-2170
Nahimana Marie RosetteWorld Health Organization, Kigali P.O. Box 1324, Rwanda.ORCID 0000-0002-1554-2094
Ramatu Usman ObansaNational Stop Transmission of Polio (NSTOP) Program, African Field Epidemiology Network (AFENET), Abuja 900103, Nigeria.
Angela Ukpojo AbahNational Stop Transmission of Polio (NSTOP) Program, African Field Epidemiology Network (AFENET), Abuja 900103, Nigeria.ORCID 0000-0002-5453-3844
Olorunsogo Bidemi AdeoyeDivision of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA.
Ester SikareGlobal Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Eugene LamGlobal Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Christopher S MurrillGlobal Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Angela Montesanti PorterGlobal Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.

Funding

CDC HHS N/AWorld Health Organization 001
6 · The paper itself

Abstract

The Expanded Programme on Immunization (EPI) and Vaccine Preventable Disease (VPD) Surveillance (VPDS) programs generate multiple data sources (e.g., routine administrative data, VPD case data, and coverage surveys). However, there are challenges with the use of these siloed data for programmatic decision-making, including poor data accessibility and lack of timely analysis, contributing to missed vaccinations, immunity gaps, and, consequently, VPD outbreaks in populations with limited access to immunization and basic healthcare services. Data triangulation, or the integration of multiple data sources, can be used to improve the availability of key indicators for identifying immunization coverage gaps, under-immunized (UI) and un-immunized (zero-dose (ZD)) children, and for assessing program performance at all levels of the healthcare system. Here, we describe the data triangulation processes, prioritization of indicators, and capacity building efforts in Bangladesh, Nigeria, and Rwanda. We also describe the analyses used to generate meaningful data, key indicators used to identify immunization coverage inequities and performance gaps, and key lessons learned. Triangulation processes and lessons learned may be leveraged by other countries, potentially leading to programmatic changes that promote improved access and utilization of vaccination services through the identification of UI and ZD children.

Indexed as

capacity buildingdata visualizationhealth equityimmunizationsurveillance

Identifiers

PMID38932375
PMCPMC11209447

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.